Towards a Secure and Reliable Federated Learning using Blockchain

TL;DR

This paper introduces SRB-FL, a blockchain-based framework that enhances the security, reliability, and scalability of federated learning by leveraging blockchain sharding and incentive mechanisms.

Contribution

It proposes a novel blockchain sharding approach and incentive mechanism to address reliability, scalability, and trust issues in federated learning.

Findings

SRB-FL improves data reliability and trustworthiness.

The framework demonstrates scalability and efficiency.

Enhanced incentive mechanisms increase device participation.

Abstract

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.